A Data Assimilation Method and its Application to Oceanography and Climate Forecasts

Resumo: A data assimilation method based on the well-known Kalman Theory is considered in conjunction with the GFDL Modular Ocean Model (MOM_2). The main purposes of the data assimaltion are to create the best possible initial condition for the ocean/atmosphere dynamical forecast models and to improve the representation and understanding of the physical state of the atmosphere-ocean system. The assimilation method proposed here is applied and verified along with the observational surface and subsurface temperatures from the PIRATA dataset.